Unsupervised feature extraction from hyperspectral images (HSI) relies on efficient data representation. However, classical data representation techniques, e.g., principal component analysis (PCA) and independent component analysis (ICA), do not reflect the intrinsic characteristics of HSI, and as such are less efficient for producing discriminative features. To address this issue, we have developed an intrinsic representation (IR) approach to support HSI classification. Based on the linear spectral mixture model (LSMM), the IR approach explains the underlying physical factors that are responsible for generating HSI. Moreover, it addresses other important characteristics of HSI, i.e., the noise variance heterogeneity effect in spectral domain and the spatial correlation effect in image domain. The IR model is solved iteratively by alternating the estimation of IR coefficients given IR bases and the update of IR bases given the coefficients. The resulting IR coefficients are discriminative, compact and noise-resistent, thereby constitute powerful features for improved HSI classification. The experiments on both simulated and real HSI demonstrate that the features extracted by the IR model are more capable of boosting the classification performance than the other referenced techniques.